
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional

import yaml

from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool


# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
lowercase__											=sys.version_info >= (3, 10)






def      UpperCamelCase_				(      A__=None  ,      A__=None						):
	return field(default_factory=lambda: default  ,      metadata=lowerCamelCase_						)







@dataclass
class   a_ :
			lowerCamelCase__	:			int
			lowerCamelCase__	:			float
			lowerCamelCase__	:			str
			lowerCamelCase__	:			bool







@dataclass
class   a_ :
			lowerCamelCase__	:			int       =    42
			lowerCamelCase__	:			str       =    field(default='toto'		,  metadata={'help': 'help message'}					)







@dataclass
class   a_ :
			lowerCamelCase__	:			bool       =    False
			lowerCamelCase__	:			bool       =    True
			lowerCamelCase__	:			Optional[bool]       =    None







class   a_ (      _SCREAMING_SNAKE_CASE					):
			lowerCamelCase__	:			List[str]       =    '''titi'''
			lowerCamelCase__	:			Optional[Any]       =    '''toto'''







class   a_ (      _SCREAMING_SNAKE_CASE					):
			lowerCamelCase__	:			List[Any]       =    '''titi'''
			lowerCamelCase__	:			Any       =    '''toto'''
			lowerCamelCase__	:			Dict       =    42







@dataclass
class   a_ :
			lowerCamelCase__	:			BasicEnum       =    "toto"




			def 					lowerCAmelCase__					(       self	):
				a_      				=						BasicEnum(self.foo	)







@dataclass
class   a_ :
			lowerCamelCase__	:			MixedTypeEnum       =    "toto"




			def 					lowerCAmelCase__					(       self	):
				a_      				=						MixedTypeEnum(self.foo	)







@dataclass
class   a_ :
			lowerCamelCase__	:			Optional[int]       =    None
			lowerCamelCase__	:			Optional[float]       =    field(default=_SCREAMING_SNAKE_CASE		,  metadata={'help': 'help message'}					)
			lowerCamelCase__	:			Optional[str]       =    None
			lowerCamelCase__	:			Optional[List[str]]       =    list_field(default=[]					)
			lowerCamelCase__	:			Optional[List[int]]       =    list_field(default=[]					)







@dataclass
class   a_ :
			lowerCamelCase__	:			List[int]       =    list_field(default=[]					)
			lowerCamelCase__	:			List[int]       =    list_field(default=[1, 2, 3]					)
			lowerCamelCase__	:			List[str]       =    list_field(default=['Hallo', 'Bonjour', 'Hello']					)
			lowerCamelCase__	:			List[float]       =    list_field(default=[0.1, 0.2, 0.3]					)







@dataclass
class   a_ :
			lowerCamelCase__	:			List[int]       =    field()
			lowerCamelCase__	:			str       =    field()
			lowerCamelCase__	:			BasicEnum       =    field()




			def 					lowerCAmelCase__					(       self	):
				a_      				=						BasicEnum(self.required_enum	)







@dataclass
class   a_ :
			lowerCamelCase__	:			int
			lowerCamelCase__	:			"BasicEnum"       =    field()
			lowerCamelCase__	:			"Optional[bool]"       =    None
			lowerCamelCase__	:			"str"       =    field(default='toto'		,  metadata={'help': 'help message'}					)
			lowerCamelCase__	:			"List[str]"       =    list_field(default=['Hallo', 'Bonjour', 'Hello']					)


if is_python_no_less_than_3_10:







		@dataclass
		class   a_ :
					lowerCamelCase__	:			bool       =    False
					lowerCamelCase__	:			bool       =    True
					lowerCamelCase__	:			bool | None       =    None







		@dataclass
		class   a_ :
					lowerCamelCase__	:			int | None       =    None
					lowerCamelCase__	:			float | None       =    field(default=_SCREAMING_SNAKE_CASE		,  metadata={'help': 'help message'}					)
					lowerCamelCase__	:			str | None       =    None
					lowerCamelCase__	:			list[str] | None       =    list_field(default=[]					)
					lowerCamelCase__	:			list[int] | None       =    list_field(default=[]					)







class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase	):
				self.assertEqual(len(a._actions	)					,   len(b._actions	)	)
				for x, y in zip(a._actions					,   b._actions	):
					a_      				=						{k: v for k, v in vars(__UpperCAmelCase	).items() if k != '''container'''}
					a_      				=						{k: v for k, v in vars(__UpperCAmelCase	).items() if k != '''container'''}

					# Choices with mixed type have custom function as "type"
					# So we need to compare results directly for equality
					if xx.get("""choices"""					,   __UpperCAmelCase	) and yy.get("""choices"""					,   __UpperCAmelCase	):
						for expected_choice in yy["choices"] + xx["choices"]:
							self.assertEqual(xx["""type"""](__UpperCAmelCase	)					,   yy["""type"""](__UpperCAmelCase	)	)
						del xx["type"], yy["type"]

					self.assertEqual(__UpperCAmelCase					,   __UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						argparse.ArgumentParser()
				expected.add_argument("""--foo"""					,   type=__UpperCAmelCase					,   required=__UpperCAmelCase	)
				expected.add_argument("""--bar"""					,   type=__UpperCAmelCase					,   required=__UpperCAmelCase	)
				expected.add_argument("""--baz"""					,   type=__UpperCAmelCase					,   required=__UpperCAmelCase	)
				expected.add_argument("""--flag"""					,   type=__UpperCAmelCase					,   default=__UpperCAmelCase					,   const=__UpperCAmelCase					,   nargs="""?"""	)
				self.argparsersEqual(__UpperCAmelCase					,   __UpperCAmelCase	)

				a_      				=						['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
				(a_		)      				=						parser.parse_args_into_dataclasses(__UpperCAmelCase					,   look_for_args_file=__UpperCAmelCase	)
				self.assertFalse(example.flag	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						argparse.ArgumentParser()
				expected.add_argument("""--foo"""					,   default=42					,   type=__UpperCAmelCase	)
				expected.add_argument("""--baz"""					,   default="""toto"""					,   type=__UpperCAmelCase					,   help="""help message"""	)
				self.argparsersEqual(__UpperCAmelCase					,   __UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						argparse.ArgumentParser()
				expected.add_argument("""--foo"""					,   type=__UpperCAmelCase					,   default=__UpperCAmelCase					,   const=__UpperCAmelCase					,   nargs="""?"""	)
				expected.add_argument("""--baz"""					,   type=__UpperCAmelCase					,   default=__UpperCAmelCase					,   const=__UpperCAmelCase					,   nargs="""?"""	)
				# A boolean no_* argument always has to come after its "default: True" regular counter-part
				# and its default must be set to False
				expected.add_argument("""--no_baz"""					,   action="""store_false"""					,   default=__UpperCAmelCase					,   dest="""baz"""	)
				expected.add_argument("""--opt"""					,   type=__UpperCAmelCase					,   default=__UpperCAmelCase	)

				a_      				=						[WithDefaultBoolExample]
				if is_python_no_less_than_3_10:
					dataclass_types.append(__UpperCAmelCase	)

				for dataclass_type in dataclass_types:
					a_      				=						HfArgumentParser(__UpperCAmelCase	)
					self.argparsersEqual(__UpperCAmelCase					,   __UpperCAmelCase	)

					a_      				=						parser.parse_args([]	)
					self.assertEqual(__UpperCAmelCase					,   Namespace(foo=__UpperCAmelCase					,   baz=__UpperCAmelCase					,   opt=__UpperCAmelCase	)	)

					a_      				=						parser.parse_args(["""--foo""", """--no_baz"""]	)
					self.assertEqual(__UpperCAmelCase					,   Namespace(foo=__UpperCAmelCase					,   baz=__UpperCAmelCase					,   opt=__UpperCAmelCase	)	)

					a_      				=						parser.parse_args(["""--foo""", """--baz"""]	)
					self.assertEqual(__UpperCAmelCase					,   Namespace(foo=__UpperCAmelCase					,   baz=__UpperCAmelCase					,   opt=__UpperCAmelCase	)	)

					a_      				=						parser.parse_args(["""--foo""", """True""", """--baz""", """True""", """--opt""", """True"""]	)
					self.assertEqual(__UpperCAmelCase					,   Namespace(foo=__UpperCAmelCase					,   baz=__UpperCAmelCase					,   opt=__UpperCAmelCase	)	)

					a_      				=						parser.parse_args(["""--foo""", """False""", """--baz""", """False""", """--opt""", """False"""]	)
					self.assertEqual(__UpperCAmelCase					,   Namespace(foo=__UpperCAmelCase					,   baz=__UpperCAmelCase					,   opt=__UpperCAmelCase	)	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						argparse.ArgumentParser()
				expected.add_argument(
				    """--foo"""					,   default="""toto"""					,   choices=["""titi""", """toto""", 42]					,   type=make_choice_type_function(["""titi""", """toto""", 42]	)					,   )
				self.argparsersEqual(__UpperCAmelCase					,   __UpperCAmelCase	)

				a_      				=						parser.parse_args([]	)
				self.assertEqual(args.foo					,   """toto"""	)
				a_      				=						parser.parse_args_into_dataclasses([]	)[0]
				self.assertEqual(enum_ex.foo					,   MixedTypeEnum.toto	)

				a_      				=						parser.parse_args(["""--foo""", """titi"""]	)
				self.assertEqual(args.foo					,   """titi"""	)
				a_      				=						parser.parse_args_into_dataclasses(["""--foo""", """titi"""]	)[0]
				self.assertEqual(enum_ex.foo					,   MixedTypeEnum.titi	)

				a_      				=						parser.parse_args(["""--foo""", """42"""]	)
				self.assertEqual(args.foo					,   42	)
				a_      				=						parser.parse_args_into_dataclasses(["""--foo""", """42"""]	)[0]
				self.assertEqual(enum_ex.foo					,   MixedTypeEnum.fourtytwo	)




			def 					lowerCAmelCase__					(       self	):







				@dataclass
				class   a_ :
							lowerCamelCase__	:			Literal["titi", "toto", 42]       =    "toto"







				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						argparse.ArgumentParser()
				expected.add_argument(
				    """--foo"""					,   default="""toto"""					,   choices=("""titi""", """toto""", 42)					,   type=make_choice_type_function(["""titi""", """toto""", 42]	)					,   )
				self.argparsersEqual(__UpperCAmelCase					,   __UpperCAmelCase	)

				a_      				=						parser.parse_args([]	)
				self.assertEqual(args.foo					,   """toto"""	)

				a_      				=						parser.parse_args(["""--foo""", """titi"""]	)
				self.assertEqual(args.foo					,   """titi"""	)

				a_      				=						parser.parse_args(["""--foo""", """42"""]	)
				self.assertEqual(args.foo					,   42	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						argparse.ArgumentParser()
				expected.add_argument("""--foo_int"""					,   nargs="""+"""					,   default=[]					,   type=__UpperCAmelCase	)
				expected.add_argument("""--bar_int"""					,   nargs="""+"""					,   default=[1, 2, 3]					,   type=__UpperCAmelCase	)
				expected.add_argument("""--foo_str"""					,   nargs="""+"""					,   default=["""Hallo""", """Bonjour""", """Hello"""]					,   type=__UpperCAmelCase	)
				expected.add_argument("""--foo_float"""					,   nargs="""+"""					,   default=[0.1, 0.2, 0.3]					,   type=__UpperCAmelCase	)

				self.argparsersEqual(__UpperCAmelCase					,   __UpperCAmelCase	)

				a_      				=						parser.parse_args([]	)
				self.assertEqual(
				    __UpperCAmelCase					,   Namespace(foo_int=[]					,   bar_int=[1, 2, 3]					,   foo_str=["""Hallo""", """Bonjour""", """Hello"""]					,   foo_float=[0.1, 0.2, 0.3]	)					,   )

				a_      				=						parser.parse_args("""--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7""".split()	)
				self.assertEqual(__UpperCAmelCase					,   Namespace(foo_int=[1]					,   bar_int=[2, 3]					,   foo_str=["""a""", """b""", """c"""]					,   foo_float=[0.1, 0.7]	)	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						argparse.ArgumentParser()
				expected.add_argument("""--foo"""					,   default=__UpperCAmelCase					,   type=__UpperCAmelCase	)
				expected.add_argument("""--bar"""					,   default=__UpperCAmelCase					,   type=__UpperCAmelCase					,   help="""help message"""	)
				expected.add_argument("""--baz"""					,   default=__UpperCAmelCase					,   type=__UpperCAmelCase	)
				expected.add_argument("""--ces"""					,   nargs="""+"""					,   default=[]					,   type=__UpperCAmelCase	)
				expected.add_argument("""--des"""					,   nargs="""+"""					,   default=[]					,   type=__UpperCAmelCase	)

				a_      				=						[OptionalExample]
				if is_python_no_less_than_3_10:
					dataclass_types.append(__UpperCAmelCase	)

				for dataclass_type in dataclass_types:
					a_      				=						HfArgumentParser(__UpperCAmelCase	)

					self.argparsersEqual(__UpperCAmelCase					,   __UpperCAmelCase	)

					a_      				=						parser.parse_args([]	)
					self.assertEqual(__UpperCAmelCase					,   Namespace(foo=__UpperCAmelCase					,   bar=__UpperCAmelCase					,   baz=__UpperCAmelCase					,   ces=[]					,   des=[]	)	)

					a_      				=						parser.parse_args("""--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3""".split()	)
					self.assertEqual(__UpperCAmelCase					,   Namespace(foo=12					,   bar=3.14					,   baz="""42"""					,   ces=["""a""", """b""", """c"""]					,   des=[1, 2, 3]	)	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						argparse.ArgumentParser()
				expected.add_argument("""--required_list"""					,   nargs="""+"""					,   type=__UpperCAmelCase					,   required=__UpperCAmelCase	)
				expected.add_argument("""--required_str"""					,   type=__UpperCAmelCase					,   required=__UpperCAmelCase	)
				expected.add_argument(
				    """--required_enum"""					,   type=make_choice_type_function(["""titi""", """toto"""]	)					,   choices=["""titi""", """toto"""]					,   required=__UpperCAmelCase					,   )
				self.argparsersEqual(__UpperCAmelCase					,   __UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						argparse.ArgumentParser()
				expected.add_argument("""--foo"""					,   type=__UpperCAmelCase					,   required=__UpperCAmelCase	)
				expected.add_argument(
				    """--required_enum"""					,   type=make_choice_type_function(["""titi""", """toto"""]	)					,   choices=["""titi""", """toto"""]					,   required=__UpperCAmelCase					,   )
				expected.add_argument("""--opt"""					,   type=__UpperCAmelCase					,   default=__UpperCAmelCase	)
				expected.add_argument("""--baz"""					,   default="""toto"""					,   type=__UpperCAmelCase					,   help="""help message"""	)
				expected.add_argument("""--foo_str"""					,   nargs="""+"""					,   default=["""Hallo""", """Bonjour""", """Hello"""]					,   type=__UpperCAmelCase	)
				self.argparsersEqual(__UpperCAmelCase					,   __UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						{
				    '''foo''': 12,
				    '''bar''': 3.14,
				    '''baz''': '''42''',
				    '''flag''': True,
				}

				a_      				=						parser.parse_dict(__UpperCAmelCase	)[0]
				a_      				=						BasicExample(**__UpperCAmelCase	)
				self.assertEqual(__UpperCAmelCase					,   __UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						{
				    '''foo''': 12,
				    '''bar''': 3.14,
				    '''baz''': '''42''',
				    '''flag''': True,
				    '''extra''': 42,
				}

				self.assertRaises(__UpperCAmelCase					,   parser.parse_dict					,   __UpperCAmelCase					,   allow_extra_keys=__UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						{
				    '''foo''': 12,
				    '''bar''': 3.14,
				    '''baz''': '''42''',
				    '''flag''': True,
				}
				with tempfile.TemporaryDirectory() as tmp_dir:
					a_      				=						os.path.join(__UpperCAmelCase					,   """temp_json"""	)
					os.mkdir(__UpperCAmelCase	)
					with open(temp_local_path + """.json"""					,   """w+"""	) as f:
						json.dump(__UpperCAmelCase					,   __UpperCAmelCase	)
					a_      				=						parser.parse_yaml_file(Path(temp_local_path + """.json"""	)	)[0]

				a_      				=						BasicExample(**__UpperCAmelCase	)
				self.assertEqual(__UpperCAmelCase					,   __UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)

				a_      				=						{
				    '''foo''': 12,
				    '''bar''': 3.14,
				    '''baz''': '''42''',
				    '''flag''': True,
				}
				with tempfile.TemporaryDirectory() as tmp_dir:
					a_      				=						os.path.join(__UpperCAmelCase					,   """temp_yaml"""	)
					os.mkdir(__UpperCAmelCase	)
					with open(temp_local_path + """.yaml"""					,   """w+"""	) as f:
						yaml.dump(__UpperCAmelCase					,   __UpperCAmelCase	)
					a_      				=						parser.parse_yaml_file(Path(temp_local_path + """.yaml"""	)	)[0]
				a_      				=						BasicExample(**__UpperCAmelCase	)
				self.assertEqual(__UpperCAmelCase					,   __UpperCAmelCase	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						HfArgumentParser(__UpperCAmelCase	)
				self.assertIsNotNone(__UpperCAmelCase	)



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


lowercase			:				Optional[int]   =     logging.getLogger(__name__)

lowercase			:				str   =     "pytorch_model.bin"







@dataclasses.dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	dataclasses.field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}		,       )







@dataclasses.dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}				)
							UpperCAmelCase_						:		str								=	dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''A csv or a json file containing the validation data.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The name of the task to train on.'''}		,       )
							UpperCAmelCase_						:		Optional[List[str]]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''The list of labels for the task.'''}				)







@dataclasses.dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	dataclasses.field(
							    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default='''accuracy'''		,       metadata={'''help''': '''The evaluation metric used for the task.'''}				)
							UpperCAmelCase_						:		Optional[str]								=	dataclasses.field(
							    default='''no'''		,       metadata={
							        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
							    }		,       )
							UpperCAmelCase_						:		Optional[int]								=	dataclasses.field(
							    default=10		,       metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,       )
							UpperCAmelCase_						:		Optional[float]								=	dataclasses.field(
							    default=0.0		,       metadata={
							        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
							    }		,       )
							UpperCAmelCase_						:		Optional[bool]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}		,       )
							UpperCAmelCase_						:		Optional[bool]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}		,       )
							UpperCAmelCase_						:		Optional[bool]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}		,       )
							UpperCAmelCase_						:		Optional[float]								=	dataclasses.field(
							    default=0.0		,       metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}		,       )
							UpperCAmelCase_						:		Optional[int]								=	dataclasses.field(
							    default=1_00		,       metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,       )
							UpperCAmelCase_						:		Optional[int]								=	dataclasses.field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Random seed for initialization.'''}		,       )






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Dict      		=					datasets.concatenate_datasets([infer_input, infer_output]     ,				axis=1		)

							if args.do_filter_by_confidence:
														A      :					Union[str, Any]      		=					dataset.filter(lambda lowerCamelCase_		: example["probability"] > args.confidence_threshold		)

							if args.do_filter_by_val_performance:
														assert eval_result >= 0.0 and eval_result <= 1.0
														A      :					Optional[int]      		=					int(eval_result * len(lowerCamelCase_		)		)
														print(lowerCamelCase_		)
														A      :					Any      		=					dataset.sort('''probability'''     ,				reverse=lowerCamelCase_		)
														A      :					Any      		=					dataset.select(range(lowerCamelCase_		)		)

							A      :					Dict      		=					dataset.remove_columns(['''label''', '''probability''']		)
							A      :					List[str]      		=					dataset.rename_column('''prediction'''     ,				'''label'''		)
							A      :					Tuple      		=					dataset.map(lambda lowerCamelCase_		: {"label": idalabel[example["label"]]}		)
							A      :					Dict      		=					dataset.shuffle(seed=args.seed		)

							A      :					str      		=					os.path.join(lowerCamelCase_     ,				F'train_pseudo.{args.data_file_extension}'		)
							if args.data_file_extension == "csv":
														dataset.to_csv(lowerCamelCase_     ,				index=lowerCamelCase_		)
							else:
														dataset.to_json(lowerCamelCase_		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				**lowerCamelCase_		):
							A      :					Optional[Any]      		=					Accelerator()
							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,				datefmt='''%m/%d/%Y %H:%M:%S'''     ,				level=logging.INFO     ,				)
							logger.info(accelerator.state		)

							# Setup logging, we only want one process per machine to log things on the
							# screen. accelerator.is_local_main_process is only True for one process per
							# machine.
							logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR		)

							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_info()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()

							A      :					List[str]      		=					STModelArguments(model_name_or_path=lowerCamelCase_		)
							A      :					List[Any]      		=					STDataArguments(train_file=lowerCamelCase_     ,				infer_file=lowerCamelCase_		)
							A      :					int      		=					STTrainingArguments(output_dir=lowerCamelCase_		)
							A      :					Any      		=					argparse.Namespace()

							for arg_class in (model_args, data_args, training_args):
														for key, value in vars(lowerCamelCase_		).items():
																					setattr(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)

							for key, value in kwargs.items():
														if hasattr(lowerCamelCase_     ,				lowerCamelCase_		):
																					setattr(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)

    # Sanity checks
							A      :					Tuple      		=					{}
							A      :					str      		=					None

							# You need to provide the training data and the data to predict on
							assert args.train_file is not None
							assert args.infer_file is not None
							A      :					Dict      		=					args.train_file
							A      :					Any      		=					args.infer_file

							if args.evaluation_strategy != IntervalStrategy.NO.value:
														assert args.eval_file is not None
														A      :					Optional[int]      		=					args.eval_file

							for key in data_files:
														A      :					str      		=					data_files[key].split('''.'''		)[-1]
														assert extension in ["csv", "json"], F'`{key}_file` should be a csv or a json file.'
														if args.data_file_extension is None:
																					A      :					List[Any]      		=					extension
														else:
																					assert extension == args.data_file_extension, F'`{key}_file` should be a {args.data_file_extension} file`.'

							assert (
							    args.eval_metric in datasets.list_metrics()
							), F'{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'

							# If passed along, set the training seed now.
							if args.seed is not None:
														set_seed(args.seed		)

							logger.info('''Creating the initial data directory for self-training...'''		)
							A      :					Any      		=					F'{args.output_dir}/self-train_iter-{{}}'.format
							A      :					Tuple      		=					data_dir_format(0		)

							if accelerator.is_main_process:
														if args.output_dir is not None:
																					os.makedirs(args.output_dir     ,				exist_ok=lowerCamelCase_		)
																					os.makedirs(lowerCamelCase_     ,				exist_ok=lowerCamelCase_		)
							accelerator.wait_for_everyone()

							A      :					str      		=					None
							A      :					List[str]      		=					None
							A      :					List[str]      		=					0
							A      :					List[Any]      		=					False
							# Show the progress bar
							A      :					Any      		=					tqdm(range(args.max_selftrain_iterations		)     ,				disable=not accelerator.is_local_main_process		)

							# Self-train
							for iteration in range(0     ,				int(args.max_selftrain_iterations		)		):
														A      :					Union[str, Any]      		=					data_dir_format(lowerCamelCase_		)
														assert os.path.exists(lowerCamelCase_		)

														# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
														# iteration > 0
														A      :					str      		=					os.path.join(lowerCamelCase_     ,				'''stage-1'''		)
														A      :					Optional[Any]      		=					{
														    '''accelerator''': accelerator,
														    '''model_name_or_path''': args.model_name_or_path,
														    '''cache_dir''': args.cache_dir,
														    '''do_train''': True,
														    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
														    '''do_eval''': True if args.eval_file is not None else False,
														    '''eval_file''': data_files['''eval'''],
														    '''do_predict''': True,
														    '''infer_file''': data_files['''infer'''],
														    '''task_name''': args.task_name,
														    '''label_list''': args.label_list,
														    '''output_dir''': current_output_dir,
														    '''eval_metric''': args.eval_metric,
														    '''evaluation_strategy''': args.evaluation_strategy,
														    '''early_stopping_patience''': args.early_stopping_patience,
														    '''early_stopping_threshold''': args.early_stopping_threshold,
														    '''seed''': args.seed,
														}
														# Add additional training arguments
														for key, value in kwargs.items():
																					if key not in arguments_dict and not hasattr(lowerCamelCase_     ,				lowerCamelCase_		):
																												arguments_dict.update({key: value}		)

														A      :					str      		=					os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''     ,				lowerCamelCase_		)
														if os.path.exists(lowerCamelCase_		):
																					logger.info(
																					    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				)
														else:
																					logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''     ,				lowerCamelCase_		)
																					finetune(**lowerCamelCase_		)
																					accelerator.wait_for_everyone()
																					assert os.path.exists(lowerCamelCase_		)
																					logger.info('''Self-training job completed: iteration: %d, stage: 1.'''     ,				lowerCamelCase_		)

														if iteration > 0 and args.finetune_on_labeled_data:
																					# Stage 2 (optional): fine-tuning on the original labeled data
																					A      :					Any      		=					os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''		)
																					A      :					int      		=					os.path.join(lowerCamelCase_     ,				'''stage-2'''		)
																					# Update arguments_dict
																					A      :					int      		=					model_path
																					A      :					Optional[Any]      		=					data_files['''train''']
																					A      :					Tuple      		=					current_output_dir

																					A      :					List[str]      		=					os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''     ,				lowerCamelCase_		)
																					if os.path.exists(lowerCamelCase_		):
																												logger.info(
																												    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				)
																					else:
																												logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''     ,				lowerCamelCase_		)
																												finetune(**lowerCamelCase_		)
																												accelerator.wait_for_everyone()
																												assert os.path.exists(lowerCamelCase_		)
																												logger.info('''Self-training job completed: iteration: %d, stage: 2.'''     ,				lowerCamelCase_		)

														A      :					List[Any]      		=					iteration
														A      :					Optional[Any]      		=					data_dir_format(iteration + 1		)

														A      :					Tuple      		=					AutoConfig.from_pretrained(os.path.join(lowerCamelCase_     ,				'''best-checkpoint'''		)		)
														A      :					List[Any]      		=					config.idalabel
														A      :					Optional[int]      		=					os.path.join(lowerCamelCase_     ,				'''eval_results_best-checkpoint.json'''		)
														A      :					Union[str, Any]      		=					os.path.join(lowerCamelCase_     ,				'''test_results_best-checkpoint.json'''		)
														assert os.path.exists(lowerCamelCase_		)

														with open(lowerCamelCase_     ,				'''r'''		) as f:
																					A      :					Optional[int]      		=					float(json.load(lowerCamelCase_		)[args.eval_metric]		)
														A      :					Any      		=					os.path.join(lowerCamelCase_     ,				'''infer_output_best-checkpoint.csv'''		)
														assert os.path.exists(lowerCamelCase_		)
														# Loading the dataset from local csv or json files.
														A      :					str      		=					load_dataset(args.data_file_extension     ,				data_files={'''data''': data_files['''infer''']}		)['''data''']
														A      :					Tuple      		=					load_dataset('''csv'''     ,				data_files={'''data''': infer_output_file}		)['''data''']

														if accelerator.is_main_process:
																					os.makedirs(lowerCamelCase_     ,				exist_ok=lowerCamelCase_		)
																					shutil.copy(lowerCamelCase_     ,				os.path.join(lowerCamelCase_     ,				F'eval_results_iter-{iteration}.json'		)		)
																					if os.path.exists(lowerCamelCase_		):
																												shutil.copy(lowerCamelCase_     ,				os.path.join(lowerCamelCase_     ,				F'test_results_iter-{iteration}.json'		)		)
																					create_pseudo_labeled_data(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
														accelerator.wait_for_everyone()

														A      :					Optional[int]      		=					os.path.join(lowerCamelCase_     ,				F'train_pseudo.{args.data_file_extension}'		)

														if args.evaluation_strategy != IntervalStrategy.NO.value:
																					A      :					Any      		=					eval_result

																					if best_iteration is None:
																												A      :					int      		=					new_iteration
																												A      :					Optional[Any]      		=					new_eval_result
																					else:
																												if new_eval_result - best_eval_result > args.early_stopping_threshold:
																																			A      :					Optional[int]      		=					new_iteration
																																			A      :					Any      		=					new_eval_result
																																			A      :					Any      		=					0
																												else:
																																			if new_eval_result == best_eval_result:
																																										A      :					Any      		=					new_iteration
																																										A      :					Any      		=					new_eval_result
																																			early_stopping_patience_counter += 1

																												if early_stopping_patience_counter >= args.early_stopping_patience:
																																			A      :					int      		=					True

														progress_bar.update(1		)

														if should_training_stop:
																					break

							if best_iteration is not None:
														# Save the best iteration
														logger.info('''Best iteration: %d'''     ,				lowerCamelCase_		)
														logger.info('''Best evaluation result: %s = %f'''     ,				args.eval_metric     ,				lowerCamelCase_		)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCamelCase_     ,				F'eval_results_iter-{iteration}.json'		)     ,				os.path.join(lowerCamelCase_     ,				'''eval_results_best-iteration.json'''		)     ,				)
							else:
														# Assume that the last iteration is the best
														logger.info('''Best iteration: %d'''     ,				args.max_selftrain_iterations - 1		)
														logger.info('''Best evaluation result: %s = %f'''     ,				args.eval_metric     ,				lowerCamelCase_		)
														accelerator.wait_for_everyone()
														if accelerator.is_main_process:
																					shutil.copy(
																					    os.path.join(lowerCamelCase_     ,				F'eval_results_iter-{args.max_selftrain_iterations - 1}.json'		)     ,				os.path.join(lowerCamelCase_     ,				'''eval_results_best-iteration.json'''		)     ,				)






